Images Of Cervix Research Articles

Time-efficient filtering of imaging polarimetric data by checking physical realizability of experimental mueller matrices.

Imaging Mueller polarimetry has already proved its potential for biomedicine, remote sensing and metrology. The real-time applications of this modality require both video rate image acquisition and fast data post-processing algorithms. First, one must check the physical realizability of the experimental Mueller matrices in order to filter out non-physical data, ie to test the positive semi-definiteness of the 4 × 4 Hermitian coherency matrix calculated from the elements of corresponding Mueller matrix pixel-wise. For this purpose, we compared the execution time for the calculations of i) eigenvalues, ii) Cholesky decomposition, iii) Sylvester's criterion, and iv) coefficients of the characteristic polynomial (two different approaches) of the Hermitian coherency matrix, all calculated for the experimental Mueller matrix images (600 pixels × 700 pixels) of mouse uterine cervix. The calculations were performed using C ++ and Julia programming languages. Our results showed the superiority of the algorithm iv) based on the simplification via Pauli matrices over other algorithms for our dataset. The sequential implementation of latter algorithm on a single core already satisfies the requirements of real-time polarimetric imaging. This can be further amplified by the proposed parallelization (e.g., we achieve a 5-fold speed up on 6 cores). The source codes of the algorithms and experimental data are available at https://github.com/pogudingleb/mueller_matrices.

EGMM: removal of specular reflection with cervical region segmentation using enhanced Gaussian mixture model in cervix images

EGMM: removal of specular reflection with cervical region segmentation using enhanced Gaussian mixture model in cervix images

TelsNet: temporal lesion network embedding in a transformer model to detect cervical cancer through colposcope images

Cervical cancer ranks as the fourth most prevalent malignancy among women globally. Timely identification and intervention in cases of cervical cancer hold the potential for achieving complete remission and cure. In this study, we built a deep learning model based on self-attention mechanism using transformer architecture to classify the cervix images to help in diagnosis of cervical cancer. We have used techniques like an enhanced multivariate gaussian mixture model optimized with mexican axolotl algorithm for segmenting the colposcope images prior to the Temporal Lesion Convolution Neural Network (TelsNet) classifying the images. TelsNet is a transformer-based neural network that uses temporal convolutional neural networks to identify cancerous regions in colposcope images. Our experiments show that TelsNet achieved an accuracy of 92.7%, with a sensitivity of 73.4% and a specificity of 82.1%. We compared the performance of our model with various state-of-the-art methods, and our results demonstrate that TelsNet outperformed the other methods. The findings have the potential to significantly simplify the process of detecting and accurately classifying cervical cancers at an early stage, leading to improved rates of remission and better overall outcomes for patients globally.

Automated Assessment of Digital Images of Uterine Cervix Captured Using Transvaginal Device-A Pilot Study.

In low-resource settings, a point-of-care test for cervical cancer screening that can give an immediate result to guide management is urgently needed. A transvaginal digital device, "Smart Scope®" (SS), with an artificial intelligence-enabled auto-image-assessment (SS-AI) feature, was developed. In a single-arm observational study, eligible consenting women underwent a Smart Scope®-aided VIA-VILI test. Images of the cervix were captured using SS and categorized by SS-AI in four groups (green, amber, high-risk amber (HRA), red) based on risk assessment. Green and amber were classified as SS-AI negative while HRA and red were classified as SS-AI positive. The SS-AI-positive women were advised colposcopy and guided biopsy. The cervix images of SS-AI-negative cases were evaluated by an expert colposcopist (SS-M); those suspected of being positive were also recommended colposcopy and guided biopsy. Histopathology was considered a gold standard. Data on 877 SS-AI, 485 colposcopy, and 213 histopathology were available for analysis. The SS-AI showed high sensitivity (90.3%), specificity (75.3%), accuracy (84.04%), and correlation coefficient (0.670, p = 0.0) in comparison with histology at the CINI+ cutoff. In conclusion, the AI-enabled Smart Scope® test is a good alternative to the existing screening tests as it gives a real-time accurate assessment of cervical health and an opportunity for immediate triaging with visual evidence.

Strawberry Cervix- A Clinical Image of Trichomonas vaginalis: A Case Report

Trichomonas vaginalis is a common cause of symptomatic vaginitis in women. Trichomoniasis occurs more frequently in people with multiple sexual partners. Women often present with vaginal discharge, painful intercourse, urinary tract infection symptoms, vaginal itching, or pelvic pain. The strawberry cervix is a finding upon physical examination where the cervix has an erythematous, punctate, and papilliform appearance. The strawberry cervix is named because of its superficially similar appearance to a strawberry. As opposed to a more general inflammation of the cervix found in cervicitis, the strawberry cervix appearance is considered to be selectively associated with Trichomoniasis. The clinical image of Trichomonas vaginalis is rarely reported. We present a clinical image of the strawberry cervix in a woman diagnosed to have trichomonas vaginalis.

Artificial neural network for cervical abnormalities detection on computed tomography images

<span lang="EN-US">Cervical cancer is the second deadliest after breast cancer in Indonesia. Sundry diagnostic imaging modalities had been used to decide the location and severity of cervical cancer, one among those is computed tomography (CT) Scan. This study handles a CT image dataset consisting of two categories, abnormal cervical images of cervical cancer patients and normal cervix images of patients with other diseases. It focuses on the ability of segmentation and classification programs to localize cervical cancer areas and classify images into normal and abnormal categories based on the features contained in them. We conferred a novel methodology for the contour detection round the cervical organ classified with artificial neural network (ANN) which was employed to categorize the image data. The segmentation algorithm used was a region-based snake model. The texture features of the cervical image area were arranged in the form of gray level co-occurrence matrix (GLCM). Support vector machine (SVM) had been added to determine which algorithm was better for comparison. Experimental results show that ANN model has better receiver operating characteristic (ROC) parameter values than SVM model’s and existing approach’s regarding 96.2% of sensitivity, 95.32% of specificity, and 95.75% of accuracy. </span>

Inter- and intra-observer agreement in the assessment of the cervical transformation zone (TZ) by visual inspection with acetic acid (VIA) and its implications for a screen and treat approach: a reliability study

BackgroundIn low-resource countries, interpretation of the transformation zone (TZ) using the classification of the International Federation for Cervical Pathology and Colposcopy (IFCPC), adopted by the World Health Organization, is critical for determining if visual inspection with acetic acid (VIA) screening and thermal ablation treatment are possible. We aim to assess inter- and intra-observer agreement in TZ interpretation.MethodsWe performed a prospective multi-observer reliability study. One hundred cervical digital images of Human papillomavirus positive women (30–49 years) were consecutively selected from a Cameroonian cervical cancer screening trial. Images of the native cervix and after VIA were obtained. The images were evaluated for the TZ type at two time points (rounds one and two) by five VIA experts from four countries (Côte d’Ivoire, Cameroon, Peru, and Zambia) according to the IFCPC classification (TZ1 = ectocervical fully visible; TZ2 = endocervical fully visible; TZ3 = not fully visible). Intra- and inter-observer agreement were measured by Fleiss’ kappa.ResultsOverall, 37.0% of images were interpreted as TZ1, 36.4% as TZ2, and 26.6% as TZ3. Global inter-observer reliability indicated fair agreement in both rounds (kappa 0.313 and 0.288). The inter-observer agreement was moderate for TZ1 interpretation (0.460), slight for TZ2 (0.153), and fair for TZ3 (0.329). Intra-observer analysis showed fair agreement for two observers (0.356 and 0.345), moderate agreement for two other (0.562 and 0.549), and one with substantial agreement (0.728).ConclusionInterpretation of the TZ using the IFCPC classification, adopted by the World Health Organization, is critical for determining if VIA screening and thermal ablation treatment are possible. However, the low inter- and intra-observer agreement suggest that the reliability of the referred classification is limited in the context of VIA. It’s integration in treatment recommendations should be used with caution since TZ3 interpretation could lead to an important referral rate for further evaluation.Trial registration Cantonal Ethics Board of Geneva, Switzerland: N°2017–0110. Cameroonian National Ethics Committee for Human Health Research N°2018/07/1083/CE/CNERSH/SP.

Transfer learning supported accurate assessment of multiclass cervix type images

Cervical cancer predominately affects women compared to lung, breast and endometrial cancer. Premature stage identification and proper treatment of this cancer may lead to 100% survival rate. The cervix type is very prominent in the detailed diagnosis of cervical cancer. High expertise and experienced gynecologist are required for an accurate diagnosis of cervical cancer. To reduce their burden, a model is proposed, based on deep learning that automatically classifies the cervix types. This paper presents Modified Deep Convolutional Neural Networks namely Modified VGG16 (MVGG16), Modified VGG19 (MVGG19), Modified ResNet50 (MRN50), Modified InceptionV3 (MIV3), and Modified InceptionResNetV2 (MIRNV2) for the classification of cervix type images. These modified networks are implemented using a Multiclass Support Vector Machine classifier. The performance metrics are tabulated and compared with pre-trained models. The simulation results show that MIRNV2 achieves the best performance compared to other models with an overall Accuracy of 92.91% and a Kappa score of 0.88. MIRNV2 model also gives better classification accuracy of 96.62% for type 1, 93.58% for type 2, and 95.61% for type 3 cervix images. Hence, this facilitates the application of MIRNV2 as a diagnostic tool to assist the gynecologist in the classification of cervix type images.

The impact of an educational tool in cervix image registration across three imaging modalities.

Accurate image registration is vital in cervical cancer where changes in both planning target volume (PTV) and organs at risk (OARs) can make decisions regarding image registration complicated. This work aims to determine the impact of a dedicated educational tool compared with experience gained in MR-guided radiotherapy (MRgRT). 10 therapeutic radiographers acted as observers and were split into two groups based on previous experience with MRgRT and Monaco treatment planning system. Three CBCT-CT, three MR-CT and two MR-MR registrations were completed per patient by each observer. Observers recorded translations, time to complete image registration and confidence. Data were collected in two phases; prior to and following the introduction of a cervix registration guide. No statistically significant differences were noted between imaging modalities. Each group was assessed independently pre- and post-education, no statistically significant differences were noted in either CBCT-CT or MR-CT imaging. Group 1 MR-MR imaging showed a statistically significant reduction in interobserver variability (p=0.04), in Group 2, the result was not statistically significant (p=0.06). Statistically significant increases in confidence were seen in all three modalities (p≤0.05). At The Christie NHS Foundation Trust, radiographers consistently registered images across three different imaging modalities regardless of their previous experience. The implementation of an image registration guide had limited impact on inter- and intraobserver variability. Radiographers' confidence showed statistically significant improvements following the use of the registration manual. This work helps evaluate training methods for novel roles that are developing in MRgRT.

Cervix Type and Cervical Cancer Classification System Using Deep Learning Techniques.

PurposeCervical cancer is the 4th most common cancer among women, worldwide. Incidence and mortality rates are consistently increasing, especially in developing countries, due to the shortage of screening facilities, limited skilled professionals, and lack of awareness. Cervical cancer is screened using visual inspection after application of acetic acid (VIA), papanicolaou (Pap) test, human papillomavirus (HPV) test and histopathology test. Inter- and intra-observer variability may occur during the manual diagnosis procedure, resulting in misdiagnosis. The purpose of this study was to develop an integrated and robust system for automatic cervix type and cervical cancer classification using deep learning techniques.Methods4005 colposcopy images and 915 histopathology images were collected from different local health facilities and online public datasets. Different pre-trained models were trained and compared for cervix type classification. Prior to classification, the region of interest (ROI) was extracted from cervix images by training and validating a lightweight MobileNetv2-YOLOv3 model to detect the transformation region. The extracted cervix images were then fed to the EffecientNetb0 model for cervix type classification. For cervical cancer classification, an EffecientNetB0 pre-trained model was trained and validated using histogram matched histopathological images.ResultsMean average precision (mAP) of 99.88% for the region of interest (ROI) extraction, and test accuracies of 96.84% and 94.5% were achieved for the cervix type and cervical cancer classification, respectively.ConclusionThe experimental results demonstrate that the proposed system can be used as a decision support tool in the diagnosis of cervical cancer, especially in low resources settings, where the expertise and the means are limited.

Analysis of digital noise and reduction methods on classifiers used in automated visual evaluation in cervical cancer screening.

The burden of cervical cancer disproportionately falls on low- and middle-income countries (LMICs). Automated visual evaluation (AVE) is a technology being considered as an adjunct tool for the management of HPV-positive women. AVE involves analysis of a white light illuminated cervical image using machine learning classifiers. It is of importance to analyze various impacts of different kinds of image degradations on AVE. In this paper, we report our work regarding the impact of one type of image degradation, Gaussian noise, and one of its remedies we have been exploring. The images, originated from the Natural History Study (NHS) and ASCUS-LSIL Triage Study (ALTS), were modified by the addition of white Gaussian noise at different levels. The AVE pipeline used in the experiments consists of two deep learning components: a cervix locator which uses RetinaNet (an object detection network), and a binary pathology classifier that uses the ResNeSt network. Our findings indicate that Gaussian noise, which frequently appears in low light conditions, is a key factor in degrading the AVE's performance. A blind image denoising technique which uses Variational Denoising Network (VDNet) was tested on a set of 345 digitized cervigram images (115 positives) and evaluated both visually and quantitatively. AVE performances on both the synthetically generated noisy images and the corresponding denoised images were examined and compared. In addition, the denoising technique was evaluated on several real noisy cervix images captured by a camera-based imaging device used for AVE that have no histology confirmation. The comparison between the AVE performances on images with and without denoising shows that denoising can be effective at mitigating classification performance degradation.

The detection of cervical neoplasia via optical ımaging: a pilot clinical study.

The present study aims to develop a new high-resolution imaging system for the early diagnosis of cervical neoplasia based on increased vessel density of the cervical tissue. An optical device was developed to obtain high contrast and resolution images of vascular structures of the cervix in the present study. The device utilizes a telecentric lens to capture cervix images under light illumination with a wavelength of 550nm emitted from LEDs. Images were obtained using the telecentric lens with or without acetic acid application to the cervix. Image processing algorithms were used to contrast and extract the skeleton of the vascular structures on the cervix. In the evaluation of the vascular density, the cervical images were divided into 12 o'clock positions, and the fractal dimension of the vascularity was calculated for each dial area between the o'clock positions. The region with the largest fractal dimension was accepted as the region with the highest probability of lesion. The range of vessel sizes was split into small classes of "bins" for each dial area with the highest fractal dimension. To validate the system's success in differentiating between normal and HSIL lesions, forty five patients who underwent colposcopy and biopsy were included in a pilot study. The system correctly classified four HSIL cases out of five and failed to detect one HSIL case, achieving an accuracy rate of 97.8% with an 80% sensitivity and 100% specificity. The developed high-resolution optical imaging system may potentially be used in detecting cervical neoplasia just before the biopsy and reduce the number of false-positive cases.

Deep learning based cervical screening by the cross-modal integration of colposcopy, cytology, and HPV test

PurposeTo develop and evaluate the colposcopy based deep learning model using all kinds of cervical images for cervical screening, and investigate the synergetic benefits of the colposcopy, the cytology test, and the HPV test for improving cervical screening performance. MethodsThis study consisted of 2160 women who underwent cervical screening, there were 442 cases with the histopathological confirmed high-grade squamous intraepithelial lesion (HSIL) or cancer, and the remained 1718 women were controls. Three kinds of cervical images were acquired from colposcopy including the saline image of cervix after saline irrigation, the acetic acid image of cervix after applying acetic acid solution, and the iodine image of cervix after applying Lugol’s iodine solution. Each kind of image was used to build a single-image based deep learning model by the VGG-16 convolutional neural network, respectively. A multiple-images based deep learning model was built using multivariable logistic regression (MLR) by combining the single-image based models. The performance of the visual inspection was also obtained. The results of the cytology test and HPV test were used to build a Cytology-HPV joint diagnostic model by MLR. Finally, a cross-modal integrated model was built using MLR by combining the multiple-images based deep learning model, the cytology test results, and the HPV test results. The performances of models were tested in an independent test set using the area under the receiver operating characteristic curve (AUC). ResultsThe saline image, acetic acid image, and iodine image based deep learning models had AUC of 0.760, 0.791, and 0.840. The multiple-images based deep learning model achieved an improved AUC of 0.845. The AUC of the visual inspection was 0.751. The Cytology-HPV joint diagnostic model had an AUC of 0.837, which was higher than the cytology test (AUC = 0.749) and the HPV test (AUC = 0.742). The cross-modal integrated model achieved the best performance with AUC of 0.921. ConclusionsCombining all kinds of cervical images were benefit for improving the performance of the colposcopy based deep learning model, and more accurate cervical screening could be achieved by incorporating the colposcopy based deep learning model, the cytology test results, and the HPV test results.

Cervix Image Classification for Prognosis of Cervical Cancer using Deep Neural Network with Transfer Learning

INTRODUCTION: Cervical cancer is the leading cancer among the other female cancers. It develops in the cervix of women. It takes decades in development thus can be preventable if diagnosed at an early stage. The cervix is classified into three types Type I/II/III. The efficacy of the treatment depends on the diagnosis of the right type of cervix. There is a thin line difference between the three types. Thus, identification of the right type of cervix becomes a difficult task for the health care providers too. To aid this problem, we proposed an algorithm based on the standard transfer learning approach used for building a model that classifies cervix images.OBJECTIVES: The objective of this study is to develop a cervical cancer predictive model based on deep learning and transfer learning techniques that will recognize and classify the cervix images into one of the classes (Type 1/Type2/Type3).METHODS: Techniques used for carrying out the experimental work includes deep learning and Transfer Learning. The three pertained models namely InceptionV3, ResNet50, and VGG19 are used for creating ConvNet that will classify the cervix images.RESULTS: The result of the experiment reveals that the Inception v3 model is performing better than Vgg19 and ResNet50 with an accuracy of 96.1% on the cervical cancer dataset.CONCLUSION: In the future, augmentation techniques can be employed to achieve better accuracy.

Network Visualization and Pyramidal Feature Comparison for Ablative Treatability Classification Using Digitized Cervix Images

Uterine cervical cancer is a leading cause of women’s mortality worldwide. Cervical tissue ablation is an effective surgical excision of high grade lesions that are determined to be precancerous. Our prior work on the Automated Visual Examination (AVE) method demonstrated a highly effective technique to analyze digital images of the cervix for identifying precancer. Next step would be to determine if she is treatable using ablation. However, not all women are eligible for the therapy due to cervical characteristics. We present a machine learning algorithm that uses a deep learning object detection architecture to determine if a cervix is eligible for ablative treatment based on visual characteristics presented in the image. The algorithm builds on the well-known RetinaNet architecture to derive a simpler and novel architecture in which the last convolutional layer is constructed by upsampling and concatenating specific RetinaNet pretrained layers, followed by an output module consisting of a Global Average Pooling (GAP) layer and a fully connected layer. To explain the recommendation of the deep learning algorithm and determine if it is consistent with lesion presentation on the cervical anatomy, we visualize classification results using two techniques: our (i) Class-selective Relevance Map (CRM), which has been reported earlier, and (ii) Class Activation Map (CAM). The class prediction heatmaps are evaluated by a gynecologic oncologist with more than 20 years of experience. Based on our observation and the expert’s opinion, the customized architecture not only outperforms the baseline RetinaNet network in treatability classification, but also provides insights about the features and regions considered significant by the network toward explaining reasons for treatment recommendation. Furthermore, by investigating the heatmaps on Gaussian-blurred images that serve as surrogates for out-of-focus cervical pictures we demonstrate the effect of image quality degradation on cervical treatability classification and underscoring the need for using images with good visual quality.

Classification of Cervical Intraepithelial Neoplasia (CIN) using fine-tuned Convolutional Neural Networks

Convolutional Neural Network (CNN) is considered one of the most successful deep learning techniques used in classification or diagnosis of medical images. However, CNN requires a high computational resource and time; and a large dataset which most medical images (cervix) do not possess. In order to compensate for these shortcomings, we propose an optimized fine-tuned CNN model to classify cervix images into Cervical Intraepithelial Neoplasia grades (CIN 1,2,3) normal and cancerous cervix images. This classification ensures that patients are diagnosed correctly, and appropriate treatments are administered. Deep learning techniques such as Data Augmentation, 1 cycle policy for optimal learning rates selection, Discriminative Fine-Tuning, Mixed Precision Training were used to optimize the fine-tuned DenseNet CNN model. The model achieved 96.3% accuracy, the specificity of 98.86%, and sensitivity of 94.97% on the datasets.

Accuracy of Multiplanar MR Images in Determination of Actual Tumour Size in Comparison to the Pelvic Examination of Carcinoma Cervix

Accurate staging of patients with cervical carcinoma is crucial for appropriate management planning and tumor size has consistently been a key component & major determinant when deciding management plan in especially with early-stages as it dictates whether a patient is a candidate for conservative surgery, radical hysterectomy or chemoradiation. Tumor size also provides a prognostic value, as larger tumors predict risk for distant spread of disease and poor prognostic outcomes. The objective of this study was to determine the sensitivity and specificity of Magnetic Resonance Imaging in the measurement of actual tumor size of invasive cervical carcinoma in comparison to pelvic examination taking histopathology as gold standard. This prospective cross sectional study was conducted in the Department of Radiology and Imaging, BSMMU, Dhaka during the period of September 2018 to August 2020. A total of 60 patients were selected purposively and all were undergone pelvic examination clinically by EUA. Then all of them underwent MRI in Department of Radiology and Imaging, BSMMU. Images of uterine cervix, corpus, vagina and parametrium are taken with a prefixed standard protocol and reporting was done by Radiologist. Comparison was done between the MRI and clinical assessment of actual tumour size. Out of 60 patients in this study the mean age was found 47.5±10.1 years with range from 22 to 60 years. Positive correlation (r=0.993; p=0.001) between histopathological size and MRI size of tumour. Positive correlation (r=0.950; p=0.001) between histopathological size and FIGO size of tumour. MRI findings more correlates with histopathology than clinically detected tumor size. Based on pathologic findings, accuracy was estimated by the degree of agreement with a difference of <0.5 or 1.0 cm between the measurements of tumor size obtained by pelvic examination and imaging modality. Pelvic examination and imaging modality had an accuracy of 53.3% and 75%, respectively, with a difference of <0.5 cm, and an accuracy of 88.3% and 100%, respectively, with a difference of 0.5-1.0 cm. The study also concluded MRI staging was more concordant with pathological stage compared to the clinical stage. Concordance rate of 95.0% in MRI and 65.0% in clinical staging respectively. FIGO staging concurred with histopathology in 39 (65.0%) cases and differed in 21 (35.0%) cases. Magnetic resonance imaging (MRI) is a sensitive and specific modality for accurate determination of tumour size as well as staging of cervical carcinoma in comparison to pelvic examination taking histopathology as gold standard.

Classification and comparison of malignancy detection of cervical cells based on nucleus and textural features in microscopic images of uterine cervix

Cervical cancer is one of the major causes of death among women worldwide. Pap smear is the cytology based screening test which is used to detect abnormal cervical cells including pre-cancerous lesions. Accurate classification of Pap smear images is one of the challenging tasks in medical image processing and its performance can be enhanced by extracting and selecting the well-defined features and classifiers. The irregular chromatin structure is one of the prominent diagnostic features of abnormal cells. Classification is performed based on the extracted textural features and the benchmark Herlev dataset features. Radial basis function (RBF), polynomial and sigmoid SVM kernels are used for classification and comparison is performed with the features in benchmark database. Precision, recall and accuracy were calculated for all the combinations of the features. The classifier gives promising results when benchmark features are combined with textural features and also benchmark nucleus features with textural features. An effective integration of features for cervical cell classification had given good results for fast abnormal cell detection and primary Pap smear cell image classification.

PREDICTING THE LOCATION OF THE UTERINE CERVICAL OS FROM 2D IMAGES WITH CNN

In an automated cervical cancer test, the prediction of the location of the cervical os from 2D images is required. Cervical os is the reference point to determine the lesion's location by either using cervical four-quadrant location or by 12 o’clock locations. Precise detection of the cervical os point ensures correct addressing of the lesions. This study used a 6-layer convolutional neural network to predict the center of the cervical os’ coordinates (x,y) on 2D grayscale images. We used a holistic approach without masking any visual element to predict the location of the cervical os. The 2D images were obtained using a telecentric lens and a CCD camera with light wavelengths of 500 550 nanometers. Because of the limited number of image samples (145 images), we used augmentation techniques to increase the training set size by rotating each original image in 1-degree increments from -30 degrees to +30 degrees relative to the center of the image. The 6-layer convolutional neural network was tested on 21 unseen cervix images using augmentation data. The outcomes showed that the image center-based augmentation technique improves the prediction performance. We obtained 2.4 RMSE in predicting the location of the cervical os.

Classification and comparison of malignancy detection of cervical cells based on nucleus and textural features in microscopic images of uterine cervix

Cervical cancer is one of the major causes of death among women worldwide. Pap smear is the cytology based screening test which is used to detect abnormal cervical cells including pre-cancerous lesions. Accurate classification of Pap smear images is one of the challenging tasks in medical image processing and its performance can be enhanced by extracting and selecting the well-defined features and classifiers. The irregular chromatin structure is one of the prominent diagnostic features of abnormal cells. Classification is performed based on the extracted textural features and the benchmark Herlev dataset features. Radial basis function (RBF), polynomial and sigmoid SVM kernels are used for classification and comparison is performed with the features in benchmark database. Precision, recall and accuracy were calculated for all the combinations of the features. The classifier gives promising results when benchmark features are combined with textural features and also benchmark nucleus features with textural features. An effective integration of features for cervical cell classification had given good results for fast abnormal cell detection and primary Pap smear cell image classification.